source/nli.py [289:314]:
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        net.train(mode=True)
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        loss_epoch += loss.item()

    print(' | loss {:e}'.format(loss_epoch), end='')

    corr, nbex = net.TestCorpus(dev_loader, 'Dev')
    if corr >= corr_best:
        print(' | saved')
        corr_best = corr
        net_best = copy.deepcopy(net)
    else:
        print('')


if 'net_best' in globals():
    if args.save != '':
        torch.save(net_best.cpu(), args.save)
    print('Best Dev: {:d} = {:5.2f}%'
          .format(corr_best, 100.0 * corr_best.float() / nbex))

    if args.gpu >= 0:
        net_best = net_best.cuda()
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source/sent_classif.py [238:263]:
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        net.train(mode=True)
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        loss_epoch += loss.item()

    print(' | loss {:e}'.format(loss_epoch), end='')

    corr, nbex = net.TestCorpus(dev_loader, 'Dev')
    if corr >= corr_best:
        print(' | saved')
        corr_best = corr
        net_best = copy.deepcopy(net)
    else:
        print('')


if 'net_best' in globals():
    if args.save != '':
        torch.save(net_best.cpu(), args.save)
    print('Best Dev: {:d} = {:5.2f}%'
          .format(corr_best, 100.0 * corr_best.float() / nbex))

    if args.gpu >= 0:
        net_best = net_best.cuda()
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